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Empirical Software Engineering

Erschienen in:

02.03.2018

Overfitting in semantics-based automated program repair

verfasst von: Xuan Bach D. Le, Ferdian Thung, David Lo, Claire Le Goues

Erschienen in: Empirical Software Engineering | Ausgabe 5/2018

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Abstract

The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbolic execution and test suites to extract semantic constraints, and uses program synthesis to synthesize repairs that satisfy the extracted constraints. Heuristic-based APR generates large populations of repair candidates via source manipulation, and searches for the best among them. Both families largely rely on a primary assumption that a program is correctly patched if the generated patch leads the program to pass all provided test cases. Patch correctness is thus an especially pressing concern. A repair technique may generate overfitting patches, which lead a program to pass all existing test cases, but fails to generalize beyond them. In this work, we revisit the overfitting problem with a focus on semantics-based APR techniques, complementing previous studies of the overfitting problem in heuristics-based APR. We perform our study using IntroClass and Codeflaws benchmarks, two datasets well-suited for assessing repair quality, to systematically characterize and understand the nature of overfitting in semantics-based APR. We find that similar to heuristics-based APR, overfitting also occurs in semantics-based APR in various different ways.

Vorheriger Artikel Improved representation and genetic operators for linear genetic programming for automated program repair

Nächster Artikel Pinpointing and repairing performance bottlenecks in concurrent programs

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http://codeforces.com/

Angelix can target multiple expressions at once; we explain the process with respect to a single buggy expression for clarity, but the technique generalizes naturally.

http://www.sygus.org/

We use the words “repair” and “patch” interchangeably.

We discuss the real-world bugs we describe qualitatively in Section 3.6.

http://codeforces.com/

Recall the explanation in Section 2.3 on the number of tests used for specification inference.

http://codeforces.com/

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Titel: Overfitting in semantics-based automated program repair
verfasst von: Xuan Bach D. Le
Ferdian Thung
David Lo
Claire Le Goues
Publikationsdatum: 02.03.2018
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 5/2018
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-017-9577-2

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